Deep Learning: A Practitioner's Approach (Paperback)

Josh Patterson, Adam Gibson



Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning—especially deep neural networks—make a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks.

Authors Adam Gibson and Josh Patterson provide theory on deep learning before introducing their open-source Deeplearning4j (DL4J) library for developing production-class workflows. Through real-world examples, you’ll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J.

  • Dive into machine learning concepts in general, as well as deep learning in particular
  • Understand how deep networks evolved from neural network fundamentals
  • Explore the major deep network architectures, including Convolutional and Recurrent
  • Learn how to map specific deep networks to the right problem
  • Walk through the fundamentals of tuning general neural networks and specific deep network architectures
  • Use vectorization techniques for different data types with DataVec, DL4J’s workflow tool
  • Learn how to use DL4J natively on Spark and Hadoop



作者Adam Gibson和Josh Patterson在介紹他們的開源Deeplearning4j(DL4J)庫用於開發生產級工作流之前,提供了有關深度學習的理論。通過實際示例,您將學習訓練深度網絡架構和在Spark和Hadoop上運行深度學習工作流的方法和策略。

- 深入研究機器學習概念,尤其是深度學習
- 了解深度網絡是如何從神經網絡基礎發展而來的
- 探索主要的深度網絡架構,包括卷積和循環網絡
- 學習如何將特定的深度網絡應用於相應的問題
- 深入了解調整一般神經網絡和特定深度網絡架構的基礎知識
- 使用DataVec進行不同數據類型的向量化技術,這是DL4J的工作流工具
- 學習如何在Spark和Hadoop上原生使用DL4J